‘It's not just to treat everybody the same’: A social justice framework for caring for larger patients in healthcare practice
Bibliographic record
Abstract
Drawing on semi-structured interviews with larger bodied patients (n = 20) and their healthcare practitioners (n = 22) in Canada, this paper combines micro and macro approaches in outlining a social justice approach to caring for larger patients in healthcare practice. Theoretically, we draw upon structural competency and critical consciousness to address the question of how social justice is enacted, experienced, and understood in interactions between clinicians and larger patients. Our findings highlight four key themes that provide a framework for integrating social justice into healthcare practice: (1) an awareness of one's simultaneous experience of marginalisation and privilege in the clinical interaction; (2) navigating between additive and interactive understandings of intersectionality; (3) micro and macro approaches to change; and (4) straddling the line between equity and equality. The synergies in participants' perspectives across social identities suggests that the cultivation of social justice awareness potentially mitigates some blinders of privilege. Furthermore, practitioners' social justice orientation positively impacted patient experience, with most patients expressing appreciation for having their various histories of trauma and social challenges handled compassionately during appointments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.035 | 0.128 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".